Triple
T36403633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuala Lumpur Sentral |
E896693
|
entity |
| Predicate | hasTaxiFacility |
P24209
|
FINISHED |
| Object | taxi terminal |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: taxi terminal | Statement: [Kuala Lumpur Sentral, hasTaxiFacility, taxi terminal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTaxiFacility Context triple: [Kuala Lumpur Sentral, hasTaxiFacility, taxi terminal]
-
A.
hasTaxiStand
chosen
Indicates that a location or facility includes or is served by a designated taxi stand area where taxis can wait for passengers.
-
B.
hasDrivingCabs
Indicates that an entity (such as a vehicle or train) is equipped with one or more driving cabs from which it can be operated or controlled.
-
C.
hasTaxiway
Indicates that an airport or runway is connected to or served by a taxiway used for aircraft ground movement.
-
D.
hasDockingFacilities
Indicates that one entity provides or is equipped with docking facilities for another entity.
-
E.
hasDropOffArea
Indicates that an entity provides a designated area where items, passengers, or goods can be temporarily left or unloaded.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e53b81081908d3b81860593f38a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:10 p.m.